Generative AI in Healthcare: Opportunities, Challenges, and Future Perspectives

Abstract

In recent years, the rapid development of AI technology Generative AI, has restructured the healthcare industry. Generative AI is a collection of algorithms that uses a large volume of medical data to generate new data in various formats, including medical images, data augmentation, and medicine development. A variety of techniques are employed in Generative AI in the healthcare industry, which includes Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), AutoRegressive Models, Flow-Based Models, and Probabilistic Graphical Models. Generative AI can applied in various domains in the healthcare sector including drug discovery, medical imaging enhancement, data augmentation, anomaly detection, simulation and training, and predictive modelling. The integration of Generative AI faces some challenges, such as addressing ethical and legal issues related to the use of Artificial Intelligence (AI) in healthcare and synthetic data in clinical decision-making, and ensuring the reliability and interpretability of AI-generated outputs.